Non-transitory computer readable medium, information processing apparatus, and attribute estimation method

ABSTRACT

There is provided a non-transitory computer readable medium storing a program causing a computer to execute a process for attribute estimation. The process includes: extracting, for each user, feature quantities of plural pieces of image information that are associated with attributes of the user; integrating the extracted feature quantities for each user; and performing learning, input of the learning being an integrated feature quantity that has been obtained as a result of integration for each user, output of the learning being one attribute, and generating a learning model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is based on and claims priority under 35 USC 119 fromJapanese Patent Application No. 2014-120377 filed Jun. 11, 2014.

BACKGROUND Technical Field

The present invention relates to a non-transitory computer readablemedium, an information processing apparatus, and an attribute estimationmethod.

SUMMARY

According to an aspect of the invention, there is provided anon-transitory computer readable medium storing a program causing acomputer to execute a process for attribute estimation. The processincludes: extracting, for each user, feature quantities of plural piecesof image information that are associated with attributes of the user;integrating the extracted feature quantities for each user; andperforming learning, input of the learning being an integrated featurequantity that has been obtained as a result of integration for eachuser, output of the learning being one attribute, and generating alearning model.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the present invention will be described indetail based on the following figures, wherein:

FIG. 1 is a block diagram illustrating an example of a configuration ofan information processing apparatus according to a first exemplaryembodiment;

FIGS. 2A to 2C are schematic diagrams for describing learning operationsperformed by the information processing apparatus;

FIG. 3 is a schematic diagram for describing attribute estimationoperations performed by the information processing apparatus;

FIG. 4 is a flowchart illustrating an example of learning operationsperformed by the information processing apparatus;

FIG. 5 is a flowchart illustrating an example of attribute estimationoperations performed by the information processing apparatus;

FIG. 6 is a block diagram illustrating an example of a configuration ofan information processing apparatus according to a second exemplaryembodiment;

FIGS. 7A and 7B are schematic diagrams for describing a method ofcreating image label information in learning operations performed by theinformation processing apparatus;

FIG. 8 is a schematic diagram illustrating a configuration of imagelabel information;

FIG. 9 is a schematic diagram for describing attribute estimationoperations performed by the information processing apparatus;

FIG. 10 is a flowchart illustrating an example of learning operationsperformed by the information processing apparatus; and

FIG. 11 is a flowchart illustrating an example of attribute estimationoperations performed by the information processing apparatus.

DETAILED DESCRIPTION

First Exemplary Embodiment

Configuration of Information Processing Apparatus

FIG. 1 is a block diagram illustrating an example of a configuration ofan information processing apparatus according to a first exemplaryembodiment.

An information processing apparatus 1 is constituted by a centralprocessing unit (CPU) and the like, and includes a controller 10 thatcontrols each unit and that executes various programs, a memory 11 thatis constituted by a storage medium, such as a flash memory, and thatstores information, and a communication unit 12 that performs externalcommunication over a network.

The controller 10 executes an attribute estimation program 110 describedbelow to thereby function as an image obtaining unit 100, an imagefeature quantity extraction unit 101, a feature quantity integrationunit 102, a learning model generation unit 103, a user attributeestimation unit 104, and the like.

The image obtaining unit 100 obtains learning image information 111 fromthe memory 11 in a learning stage. The learning image information 111 isimage information that has been posted on a social networking service(SNS), and is image information for learning to which attributes of auser typically including sex, age, occupation, and the like have beenassigned in advance as verified ones. In addition to information storedin the memory 11, the learning image information 111 may be informationthat has been obtained from outside or that has been transmitted andreceived from outside via the communication unit 12, or may beinformation prepared by manually assigning attributes to imageinformation to which attributes have not been assigned in advance.

Furthermore, the image obtaining unit 100 obtains image information 116from the memory 11 in an attribute estimation stage. It is assumed thatthe image information 116 is image information posted on an SNS, butattributes of a user who has posted the image information have not beenassigned and are unknown. The image obtaining unit 100 not only obtainsthe image information 116 from the memory 11, but may also receive imageinformation obtained from outside or transmitted from outside via thecommunication unit 12.

The image feature quantity extraction unit 101 extracts featurequantities from the learning image information 111 or the imageinformation 116 obtained by the image obtaining unit 100. The imagefeature quantity extraction unit 101 stores the feature quantities inthe memory 11 as feature quantity information 112. For example, theimage feature quantity extraction unit 101 first extracts a localfeature quantity using scale-invariant feature transform (SIFT) whenextracting a feature quantity, performs clustering on the extractedlocal feature quantity using k-means, and takes K cluster centers thathave been obtained as codewords. Next, the image feature quantityextraction unit 101 generates a bag-of-features (BoF) histogram for aneighbor codeword using a k-nearest neighbor algorithm and spatialpyramid matching (SPM), and assumes the histogram to be a featurequantity.

The feature quantity integration unit 102 integrates the featurequantities extracted by the image feature quantity extraction unit 101for each user, and generates integrated feature quantity information113. The feature quantity integration unit 102 adds up, for each user,BoF histograms that are feature quantities, for example, and obtains theintegrated feature quantity information 113 by performing normalizationusing the number of feature quantities.

The learning model generation unit 103 performs learning, the input ofthe learning being the integrated feature quantity information 113 thathas been generated by the feature quantity integration unit 102integrating, for each user, the feature quantities extracted from thelearning image information 111, the output of the learning beingattributes of the user, and generates a learning model 114. The learningmodel generation unit 103 uses an algorithm, such as a support vectormachine (SVM), for example, when performing learning.

The user attribute estimation unit 104 estimates, by using the learningmodel 114, attribute information from the integrated feature quantityinformation 113 that has been generated by the feature quantityintegration unit 102 integrating, for each user, the feature quantitiesextracted from the image information 116, and generates user attributeinformation 117 that is associated with the user.

The memory 11 stores the attribute estimation program 110 that causesthe controller 10 to operate as the image obtaining unit 100, the imagefeature quantity extraction unit 101, the feature quantity integrationunit 102, the learning model generation unit 103, and the user attributeestimation unit 104, the learning image information 111, the featurequantity information 112, the integrated feature quantity information113, the learning model 114, user information 115, the image information116, the user attribute information 117, and the like.

The user information 115 is information, such as a user identification(ID), for identifying a user who uses an SNS.

Note that the learning image information 111, the user information 115,the image information 116, and the user attribute information 117 may beobtained from an external SNS server via the communication unit 12.

Operations Performed by Information Processing Apparatus

Next, actions in the first exemplary embodiment will be described for(1) learning operations and (2) attribute estimation operationsseparately.

(1) Learning Operations

FIG. 4 is a flowchart illustrating an example of learning operationsperformed by the information processing apparatus 1. FIGS. 2A to 2C areschematic diagrams for describing learning operations performed by theinformation processing apparatus 1.

First, the image obtaining unit 100 obtains from the memory 11 thelearning image information 111 regarding users who have a specificattribute (step S1).

For example, in examples illustrated in FIGS. 2A to 2C, pieces of imageinformation 111 a ₁, 111 a ₂, 111 a ₃, and so on are image informationposted by a user 115 a, pieces of image information 111 b ₁, 111 b ₂,111 b ₃, and so on are image information posted by a user 115 b, andpieces of image information 111 c ₁, 111 c ₂, 111 c ₃, and so on areimage information posted by a user 115 c, and the users 115 a to 115 chave been assigned in advance respective attributes 111 a _(t) to 111 c_(t).

A set of the attributes 111 a _(t) and the pieces of image information111 a ₁, 111 a ₂, 111 a ₃, and so on, a set of the attributes 111 b _(t)and the pieces of image information 111 b ₁, 111 b ₂, 111 b ₃, and soon, and a set of the attributes 111 c _(t) and the pieces of imageinformation 111 c ₁, 111 c ₂, 111 c ₃, and so on described above are thelearning image information 111. If “male” has been selected as aspecific attribute, for example, the image obtaining unit 100 obtainsthe pieces of image information 111 a ₁, 111 a ₂, 111 a ₃, and so onregarding the user 115 a and the pieces of image information 111 c ₁,111 c ₂, 111 c ₃, and so on regarding the user 115 c. Note that aspecific attribute may be selected by an administrator of theinformation processing apparatus 1, or the information processingapparatus 1 may select “male”, “female”, and so on in order.

The image obtaining unit 100 may obtain image information regarding auser to which attributes have not been assigned in advance, theattributes being assigned by the user or an administrator of theinformation processing apparatus 1 thereafter, and may handle the imageinformation and the attributes as the learning image information 111.

Next, the image feature quantity extraction unit 101 extracts featurequantities respectively from the pieces of image information 111 a ₁,111 a ₂, 111 a ₃, and so on and the pieces of image information 111 c ₁,111 c ₂, 111 c ₃, and so on that have been obtained by the imageobtaining unit 100 (step S2). The image feature quantity extraction unit101 stores the feature quantities in the memory 11 as the featurequantity information 112.

Next, the feature quantity integration unit 102 integrates the featurequantities extracted by the image feature quantity extraction unit 101for each user, and generates the integrated feature quantity information113 (step S3). That is, the feature quantities extracted from the piecesof image information 111 a ₁, 111 a ₂, 111 a ₃, and so on are integratedand assumed to be integrated feature quantity information 113 aregarding the user 115 a, and the feature quantities extracted from thepieces of image information 111 c ₁, 111 c ₂, 111 c ₃, and so on areintegrated and assumed to be integrated feature quantity information 113c regarding the user 115 c.

Next, the learning model generation unit 103 performs learning, theinput of the learning being the integrated feature quantity information113 a and 113 c, the output of the learning being an attribute of theusers, that is, “male”, generates the learning model 114 (step S4), andstores the learning model 114 in the memory 11 (step S5).

Next, attribute estimation operations using the above-described learningmodel 114 will be described.

(2) Attribute Estimation Operations

FIG. 5 is a flowchart illustrating an example of attribute estimationoperations performed by the information processing apparatus 1. FIG. 3is a schematic diagram for describing attribute estimation operationsperformed by the information processing apparatus 1.

First, the image obtaining unit 100 refers to the user information 115,as illustrated in FIG. 3, determines a user 115 n to be a user who is tobe a target of attribute estimation, and obtains pieces of imageinformation 116 n ₁, 116 n ₂, 116 n ₃, and so on that have been postedby the user 115 n, from the memory 11 (step S11). It is assumed thatattributes 111 n _(t) of the user 115 n are unknown. The image obtainingunit 100 may receive plural pieces of image information transmitted froma user for which attribute estimation is desired, and may assume theuser to be a target of attribute estimation. That is, the imageobtaining unit 100 need not refer to the user information 115.

Next, the image feature quantity extraction unit 101 extracts featurequantities respectively from the pieces of image information 116 n ₁,116 n ₂, 116 n ₃, and so on that have been obtained by the imageobtaining unit 100 (step S12).

Next, the feature quantity integration unit 102 integrates the featurequantities extracted by the image feature quantity extraction unit 101,and generates the integrated feature quantity information 113 (stepS13). That is, the feature quantities extracted from the pieces of imageinformation 116 n ₁, 116 n ₂, 116 n ₃, and so on are integrated andassumed to be integrated feature quantity information 113 n regardingthe user 115 n.

Next, the user attribute estimation unit 104 estimates attributeinformation 117 n from the integrated feature quantity information 113n, by using the learning model 114 generated as described in “(1)Learning Operations” (step S14), and, if an attribute “male” isobtained, stores the attribute in the memory 11 as the user attributeinformation 117 while associating the attribute with the user 115 n(step S15).

Second Exemplary Embodiment

A second exemplary embodiment is different from the first exemplaryembodiment in that learning is performed by taking into considerationnot only user attributes but also labels assigned to image information,and an attribute of a user who has posted image information is estimatedon the basis of the result of the learning.

FIG. 6 is a block diagram illustrating an example of a configuration ofan information processing apparatus according to the second exemplaryembodiment.

An information processing apparatus 2 is constituted by a CPU and thelike, and includes a controller 20 that controls each unit and thatexecutes various programs, a memory 21 that is constituted by a storagemedium, such as a flash memory, and that stores information, and acommunication unit 22 that performs external communication over anetwork.

The controller 20 executes an attribute estimation program 210 describedbelow to thereby function as an image obtaining unit 200, an imagefeature quantity extraction unit 201, an image label assigning unit 202,a learning model generation unit 203, an image label estimation unit204, a user attribute estimation unit 205, and the like.

The image obtaining unit 200 has functions similar to the imageobtaining unit 100 in the first exemplary embodiment. The image featurequantity extraction unit 201 has functions similar to the image featurequantity extraction unit 101 in the first exemplary embodiment. Theimage feature quantity extraction unit 201 stores feature quantitiesthat have been extracted in the memory 21 as feature quantityinformation 212.

The image label assigning unit 202 assigns image label information 213that is generated by combining user attributes and image contents inaccordance with the contents of learning image information 211.

The learning model generation unit 203 performs learning, the input ofthe learning being feature quantities that have been extracted by theimage feature quantity extraction unit 201 from the learning imageinformation 211, the output of the learning being image labels assignedto the learning image information 211, and generates a learning model214.

The image label estimation unit 204 calculates, by using the learningmodel 214, scores of the image labels from feature quantities that havebeen extracted by the image feature quantity extraction unit 201 fromimage information 216, and estimates image labels to be associated withthe image information 216 on the basis of the scores.

The user attribute estimation unit 205 integrates the image labels thathave been estimated by the image label estimation unit 204 for eachuser, estimates an attribute of the user by comparing the scores ofrespective attributes, and generates user attribute information 217 thatis associated with the user.

The memory 21 stores the attribute estimation program 210 that causesthe controller 20 to operate as the image obtaining unit 200, the imagefeature quantity extraction unit 201, the image label assigning unit202, the learning model generation unit 203, the image label estimationunit 204, and the user attribute estimation unit 205, the learning imageinformation 211, the feature quantity information 212, the image labelinformation 213, the learning model 214, user information 215, the imageinformation 216, the user attribute information 217, and the like.

Operations Performed by Information Processing Apparatus

Next, actions in the second exemplary embodiment will be described for(1) learning operations and (2) attribute estimation operationsseparately.

(1) Learning Operations

FIG. 10 is a flowchart illustrating an example of learning operationsperformed by the information processing apparatus 2. FIGS. 7A and 7B areschematic diagrams for describing a method of creating the image labelinformation 213 in learning operations performed by the informationprocessing apparatus 2. FIG. 8 is a schematic diagram illustrating aconfiguration of the image label information 213.

First, the image label assigning unit 202 accepts selection of anattribute type for which learning (estimation) is desired (step S31).Description will be given below while assuming that, as illustrated inFIG. 7A, there are attribute types including an attribute type 217 athat indicates “sex”, an attribute type 217 b that indicates “age”, andso on, and that the attribute type 217 a that indicates “sex” has beenselected by an administrator or the like.

Next, the image label assigning unit 202 combines the attribute type 217a that has been selected and image contents 213 a illustrated in FIG.7B, and creates the image label information 213 illustrated in FIG. 8(step S32). The image label information 213 is obtained by combiningattributes included in the attribute type 217 a and the image contents213 a, and therefore, 30 image labels are created, the number “30” beingobtained by multiplying the number of attributes “3” by the number oflabels “10”.

Next, the image label assigning unit 202 assigns the created imagelabels of the image label information 213 to the learning imageinformation 211 in accordance with operations performed by anadministrator or the like (step S33). Note that the learning imageinformation 211 to which image labels have been assigned in advance maybe prepared. Furthermore, a configuration may be employed in whichfeature quantities of the learning image information 211 are extracted,clustering is performed on the learning image information 211 on thebasis of the feature quantities, the image label information 213 iscreated by using names, such as “class 1”, “class 2”, “class 3”, and soon, that are based on the clustering classification, instead of usingthe image contents 213 a, and the image labels are automaticallyassigned.

Next, the image feature quantity extraction unit 201 extracts featurequantities from the learning image information 211 (step S34). The imagefeature quantity extraction unit 201 stores the feature quantities inthe memory 21 as the feature quantity information 212.

Next, the learning model generation unit 203 performs learning, theinput of the learning being the feature quantities that have beenextracted by the image feature quantity extraction unit 201 from thelearning image information 211, the output of the learning being imagelabels assigned to the learning image information 211, generates thelearning model 214 (step S35), and stores the learning model 214 in thememory 21 (step S36).

(2) Attribute Estimation Operations

FIG. 11 is a flowchart illustrating an example of attribute estimationoperations performed by the information processing apparatus 2. FIG. 9is a schematic diagram for describing attribute estimation operationsperformed by the information processing apparatus 2.

First, the image obtaining unit 200 refers to the user information 215,determines a user who is to be a target of attribute estimation, andobtains pieces of image information posted by the user, from the memory21 (step S41). It is assumed that attributes of the user are unknown.

Next, the image feature quantity extraction unit 201 extracts featurequantities from the pieces of image information obtained by the imageobtaining unit 200 (step S42). The image feature quantity extractionunit 201 stores the feature quantities in the memory 21 as the featurequantity information 212.

Next, the image label estimation unit 204 calculates, by using thelearning model 214 generated as described in “(1) Learning Operations”,scores that are estimation values, each indicating the degree ofmatching with a corresponding image label, as illustrated in FIG. 9,from the feature quantities that have been extracted by the imagefeature quantity extraction unit 201 from the image information 216, andobtains score calculation results 204 a (step S43). In an exampleillustrated in FIG. 9, the score calculation results 204 a are resultsof calculation of the scores of all image labels, and items in the scorecalculation results 204 a are sorted in descending order of score.

Next, the user attribute estimation unit 205 integrates the scores ofimage labels for each attribute on the basis of the score calculationresults 204 a (step S44). For example, the scores of image labels thatinclude “female” are added up, and the score of the attribute “female”is obtained. The scores of image labels that include “male” are addedup, and the score of the attribute “male” is obtained. Similarly, thescores of image labels that include “unknown” are added up, and thescore of the attribute “unknown” is obtained. Note that a method ofintegrating scores is not limited to a method using addition, and may bea method in which the highest score is selected for each attribute ormay be based on other calculation methods.

Next, in a case where the score of the attribute “female”, which is3.56, the score of the attribute “male”, which is 2.11, and the score ofthe attribute “unknown”, which is 0.22, are obtained, for example, theuser attribute estimation unit 205 compares these values, estimates thatthe attribute “female” that has the highest score is an attribute of theuser (step S45), and stores the attribute in the memory 21 as the userattribute information 217 while associating the attribute with the user(step S46).

In a case where an attribute is not alternatively determined but mayhave plural values, the user attribute estimation unit 205 estimateseach attribute, an integrated value of which exceeds a predeterminedthreshold, to be an attribute of the user.

Other Exemplary Embodiments

Note that the present invention is not limited to the exemplaryembodiments described above, and various modifications may be madewithout departing from the spirit of the present invention. In the firstexemplary embodiment, while the functions of the image obtaining unit100, the image feature quantity extraction unit 101, the featurequantity integration unit 102, the learning model generation unit 103,and the user attribute estimation unit 104 of the controller 10 areimplemented only by the information processing apparatus 1, some of thefunctions may be implemented by other server apparatuses or terminalapparatuses. Similarly, in the second exemplary embodiment, some of thefunctions of the image obtaining unit 200, the image feature quantityextraction unit 201, the image label assigning unit 202, the learningmodel generation unit 203, the image label estimation unit 204, and theuser attribute estimation unit 205 of the controller 20 may beimplemented by other server apparatuses or terminal apparatuses.

The learning image information 111, the feature quantity information112, the integrated feature quantity information 113, the learning model114, the user information 115, the image information 116, and the userattribute information 117 need not be stored in the memory 11 of theinformation processing apparatus 1, and the learning image information211, the feature quantity information 212, the image label information213, the learning model 214, the user information 215, the imageinformation 216, and the user attribute information 217 need not bestored in the memory 21 of the information processing apparatus 2. Thesepieces of information may be obtained from an external database or anexternal apparatus, or may be transmitted and received from an externalapparatus without being stored in the memory 11 or the memory 21, andmay be used by each unit.

In the exemplary embodiments described above, while the functions of theimage obtaining unit 100, the image feature quantity extraction unit101, the feature quantity integration unit 102, the learning modelgeneration unit 103, and the user attribute estimation unit 104 of thecontroller 10, and the functions of the image obtaining unit 200, theimage feature quantity extraction unit 201, the image label assigningunit 202, the learning model generation unit 203, the image labelestimation unit 204, and the user attribute estimation unit 205 of thecontroller 20 are implemented by the programs, all or some of the unitsmay be implemented by hardware, such as an application-specificintegrated circuit (ASIC). The programs used in the above-describedexemplary embodiments may be stored in a recording medium, such as acompact disc read-only memory (CD-ROM), and provided. Furthermore, thesteps described in the above exemplary embodiments may be interchanged,deleted, or added, for example, without changing the spirit of thepresent invention.

The foregoing description of the exemplary embodiments of the presentinvention has been provided for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Obviously, many modificationsand variations will be apparent to practitioners skilled in the art. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, therebyenabling others skilled in the art to understand the invention forvarious embodiments and with the various modifications as are suited tothe particular use contemplated. It is intended that the scope of theinvention be defined by the following claims and their equivalents.

What is claimed is:
 1. A non-transitory computer readable medium storinga program causing a computer to execute a process for attributeestimation, the process comprising: extracting, for each user, featurequantities of a plurality of image information that are associated withattributes of the user, the user being a user that posted imageinformation, wherein the posted image information are icons relating tointerests of the user; integrating the extracted feature quantities foreach user; and performing learning, input of the learning being anintegrated feature quantity that has been obtained as a result ofintegration for each user, output of the learning being one attribute ofthe user that posted the image information, and generating a learningmodel, wherein each of the plurality of image information are scored andthe attribute of the user is estimated based upon whether or not thescore of the plurality of image information reaches a threshold.
 2. Anon-transitory computer readable medium storing a program causing acomputer to execute a process for attribute estimation, the processcomprising: extracting, for attribute estimation, feature quantities ofa plurality of image information that are associated with a user, theuser being a user that posted image information, wherein the postedimage information are icons relating to interests of the user;integrating the feature quantities that have been extracted forattribute estimation; and estimating an attribute of the user thatposted the image information from an integrated feature quantity thathas been obtained as a result of integration of the feature quantitiesextracted for attribute estimation, by using a learning model, whereineach of the plurality of image information are scored and the attributeof the user is estimated based upon whether or not the score of theplurality of image information reaches a threshold.
 3. A non-transitorycomputer readable medium storing a program causing a computer to executea process for attribute estimation, the process comprising: extractingfeature quantities of a plurality of image information that areassociated with combinations of user attributes and image contents, theuser attributes and image contents being of a user that posted imageinformation, wherein the posted image information are icons relating tointerests of the user; and performing learning, input of the learningbeing the extracted feature quantities, output of the learning beingcombinations of one user attribute and the image contents, the userattribute and image contents being of the user that posted the imageinformation, and generating a learning model, wherein each of theplurality of image information are scored and the user attribute isestimated based upon whether or not the score of the plurality of imageinformation reaches a threshold.
 4. A non-transitory computer readablemedium storing a program causing a computer to execute a process forattribute estimation, the process comprising: extracting, for attributeestimation, feature quantities of a plurality of image information thatare associated with a user, the user being a user that posted imageinformation, wherein the posted image information are icons relating tointerests of the user; estimating, from the feature quantities extractedfor attribute estimation, matching degrees of combinations of userattributes and image contents by using a learning model, as estimationvalues; and calculating estimation values of respective attributes byintegrating, for each of the attributes, the estimation values thatindicate the matching degrees of the combinations of the user attributesand the image contents, and estimating an attribute of the user thatposted the image information in accordance with the estimation values ofthe respective attributes, wherein each of the plurality of imageinformation are scored and the attribute of the user is estimated basedupon whether or not the score of the plurality of image informationreaches a threshold.
 5. An information processing apparatus comprising:at least one processor configured to execute: an extraction unit thatextracts, for each user, feature quantities of a plurality of imageinformation that are associated with attributes of the user, the userbeing a user that posted image information, wherein the posted imageinformation are icons relating to interests of the user; a featurequantity integration unit that integrates the feature quantitiesextracted by the extraction unit for each user; and a learning unit thatperforms learning, input of the learning being an integrated featurequantity that has been obtained as a result of integration performed bythe feature quantity integration unit for each user, output of thelearning being one attribute of the user that posted the imageinformation, and generates a learning model, wherein each of theplurality of image information are scored and the attribute of the useris estimated based upon whether or not, the score of the plurality ofimage information reaches a threshold.
 6. An information processingapparatus comprising: at least one processor configured to execute: anextraction unit that extracts, for attribute estimation, featurequantities of a plurality of image information that are associated witha user, the user being a user that posted image information wherein theposted image information are icons relating to interests of the user; afeature quantity integration unit that integrates the feature quantitiesthat have been extracted for attribute estimation; and an attributeestimation unit that estimates an attribute of the user that posted theimage information from an integrated feature quantity that has beenobtained as a result of integration of the feature quantities extractedfor attribute estimation, the integration having been performed by thefeature quantity integration unit, by using a learning model, whereineach of the plurality of image information are scored and the attributeof the user is further estimated based upon whether or not the score ofthe plurality of image information reaches a threshold.
 7. Aninformation processing apparatus comprising: at least one processorconfigured to execute: an extraction unit that extracts featurequantities of a plurality of image information that are associated withcombinations of user attributes and image contents, the user attributesand image contents being of a user that posted image information,wherein the posted image information are icons relating to interests ofthe user; and a learning unit that performs learning, input of thelearning being the feature quantities extracted by the extraction unit,output of the learning being combinations of one user attribute and theimage contents, the user attribute and image contents being of the userthat posted the image information, and generates a learning model,wherein each of the plurality of image information are scored and userattribute is estimated based upon whether or not the score of theplurality of image information reaches a threshold.
 8. An informationprocessing apparatus comprising: at least one processor configured toexecute: an extraction unit that extracts, for attribute estimation,feature quantities of a plurality of image information that areassociated with a user, the user being a user that posted imageinformation, wherein the posted image information are icons relating tointerests of the user; a label estimation unit that estimates, from thefeature quantities extracted for attribute estimation, matching degreesof combinations of user attributes and image contents by using alearning model, as estimation values; and an attribute estimation unitthat calculates estimation values of respective attributes byintegrating, for each of the attributes, the estimation values thatindicate the matching degrees of the combinations of the user attributesand the image contents, and estimates an attribute of the user thatposted the image information in accordance with the estimation values ofthe respective attributes, wherein each of the plurality of imageinformation are scored and the attribute of the user is furtherestimated based upon whether or not the score of the plurality of imageinformation reaches a threshold.
 9. An attribute estimation methodcomprising: extracting, for each user, feature quantities of a pluralityof image information that are associated with attributes of the user,the user being a user that posted image information, wherein the postedimage information are icons relating to interests of the user;integrating the extracted feature quantities for each user; andperforming learning, input of the learning being an integrated featurequantity that has been obtained as a result of integration for eachuser, output of the learning being one attribute of the user that postedthe image information, and generating a learning model, wherein each ofthe plurality of image information are scored and the one attribute ofthe user is estimated based upon whether or not the score of theplurality of image information reaches a threshold.